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With the advent of deep learning, various deep neural network architectures have been proposed to capture the complex spatio-temporal dependencies in traffic data. This paper introduces a novel Deep Bi-directional Adaptive Gating Graph Convolutional Network (DBAG-GCN) model for spatio-temporal traffic forecasting. The proposed model leverages the power of graph convolutional networks to capture the spatial dependencies in the road network topology and incorporates bi-directional gating mechanisms to control the information flow adaptively. Furthermore, we introduce a multi-scale temporal convolution module to capture multi-scale temporal dynamics and a contextual attention mechanism to integrate external factors such as weather conditions and event information. Extensive experiments on real-world traffic datasets demonstrate the superior performance of DBAG-GCN compared to state-of-the-art baselines, achieving significant improvements in prediction accuracy and computational efficiency. The DBAG-GCN model provides a powerful and flexible framework for spatio-temporal traffic forecasting, paving the way for intelligent transportation management and urban planning.
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